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Susa Fiedler

@susafiedler.bsky.social

Behavioral Social (Meta) Scientist @wu_vienna - Department of Strategy and Innovation | Curious about the processes underlying #DecisionMaking #OpenScience #Inequality #Wellbeing

11 Posts482 Follower180 Folgt
Johannes Haushofer@johanneshaushofer.com · 3y ago

We started @malengo.org in 2021 to support Ugandan students on their journey to a Bachelor's degree in Germany. We admitted 10 students in 2021; 30 in 2022; and 120 in 2023. Our next goal: FOUR HUNDRED! We urgently need both investors and donors — please read and spread widely! 1/n

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Dr Will Ball@wpball.com · 3y ago

Great accessible paper on causal inference methods here. Absolutely brilliant R code in the associated github repo too github.com/ArthurChatton/CausalCookbook Chapeau @achatton.bsky.social @dingdingpeng.bsky.social One for Episky folks

GitHub - ArthurChatton/CausalCookbook

Contribute to ArthurChatton/CausalCookbook development by creating an account on GitHub.

github.com

Julia M. Rohrer@dingdingpeng.the100.ci · 3y ago

New preprint! There are lots of interesting estimators that can be used to target causal effects, some of which aren't well-known in psychology. Arthur Chatton and I provide a (gentle) introduction to approaches commonly used in epidemiology: psyarxiv.com/k2gzp

The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization Abstract: Recent developments in the causal inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do we actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and non-interference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses...